We refer to [1]} for a review of Variational Inference (VI). Our focus is to use boosting to increase the complexity of a density, similar to the goal of Normalizing Flows [2]}, MCMC-VI hybrid methods [3]}, [4]}, distribution transformations [5]}, and boosted density estimation
[6]}, [7]}.
Our approach is in line with several previous ones using mixtures of distributions to improve the expressiveness of the variational approximation [8]}, [9]} but goes further to draw connections to the optimization literature.
While our method does not leverage classical weak learners as in [6]}, it does return a solution which is sampleable and is therefore more amenable to downstream Bayesian analysis.
